Enablers of Physician Prescription of a Long-Term Asthma Controller in Patients with Persistent Asthma
Bibliographic record
Abstract
Objective. We aimed to identify key enablers of physician prescription of a long-term controller in patients with persistent asthma. Methods. We conducted a mailed survey of randomly selected Quebec physicians. We sent a 102-item questionnaire, seeking reported management regarding one of 4 clinical vignettes of a poorly controlled adult or child and endorsement of enablers to prescribe long-term controllers. Results. With a 56% participation rate, 421 physicians participated. Most (86%) would prescribe a long-term controller (predominantly inhaled corticosteroids, ICS) to the patient in their clinical vignette. Determinants of intention were the recognition of persistent symptoms (OR 2.67), goal of achieving long-term control (OR 5.31), and high comfort level in initiating long-term ICS (OR 2.33). Decision tools, pharmacy reports, reminders, and specific training were strongly endorsed by ≥60% physicians to support optimal management. Physicians strongly endorsed asthma education, lung function testing, specialist opinion, accessible asthma clinic, and paramedical healthcare professionals to guide patients, as enablers to improve patient adherence to and physicians' comfort with long-term ICS. Interpretation. Tools and training to improve physician knowledge, skills, and perception towards long-term ICS and resources that increase patient adherence and physician comfort to facilitate long-term ICS prescription should be considered as targets for implementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".